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Probabilistic Points-to Analysis

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Languages and Compilers for Parallel Computing (LCPC 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2624))

Abstract

Information gathered by the existing pointer analysis techniques can be classified as must aliases or definitely-points-to relationships, which hold for all executions, and may aliases or possibly-points-to relationships, which might hold for some executions. Such information does not provide quantitative descriptions to tell how likely the conditions will hold for the executions, which are needed for modern compiler optimizations, and thus has hindered compilers from more aggressive optimizations. This paper addresses this issue by proposing a probabilistic points-to analysis technique to compute the probability of each points-to relationship. Initial experiments are done by incorporating the probabilistic data flow analysis algorithm into SUIF and MachSUIF, and preliminary experimental results show the probability distributions of points-to relationships in several benchmark programs. This work presents a major enhancement for pointer analysis to keep up with modern compiler optimizations.

The work was supported in part by NSC of Taiwan under grant no. NSC-89-2213-E-019-019, NSC-90-2213-E-019-016, NSC-89-2218-E-007-023, NSC-89-2219-E-007-012, and MOE research excellent project under grant no. 89-E-FA04-1-4.

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Author information

Authors and Affiliations

  1. Department of Computer Science, National Taiwan Ocean University, Keelung, 202, Taiwan

    Yuan-Shin Hwang

  2. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan

    Peng-Sheng Chen & Jenq Kuen Lee

  3. Microprocessor Research Lab., Intel Corporation, Santa Clara, CA, 95052, USA

    Roy Dz-Ching Ju

Authors
  1. Yuan-Shin Hwang
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  2. Peng-Sheng Chen
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  3. Jenq Kuen Lee
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  4. Roy Dz-Ching Ju
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Kentucky, Lexington, KY, 40506-0046, USA

    Henry G. Dietz

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© 2003 Springer-Verlag Berlin Heidelberg

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Hwang, YS., Chen, PS., Lee, J.K., Ju, R.DC. (2003). Probabilistic Points-to Analysis. In: Dietz, H.G. (eds) Languages and Compilers for Parallel Computing. LCPC 2001. Lecture Notes in Computer Science, vol 2624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35767-X_19

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  • DOI: https://doi.org/10.1007/3-540-35767-X_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04029-3

  • Online ISBN: 978-3-540-35767-4

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